Is there any way to see alphas/coefs/intercept associated with *all* scenarios tested within ElasticNetCV

### Describe the workflow you want to enable

I like that ElasticNetCV outputs the MSE path for CV folds/alphas but is there any way to similarly track associated model params (ie, coef/intercept) for each scenario and include them as part of output.

I get that it's easier to just output 'best' estimators/params but would be useful to add granularity to allow identifying a 'sweet spot', either via MSE curve or something else, which would make outputting all params additive.   



### Describe your proposed solution

As described, run existing scenarios as is but instead of holding only through evaluation of 'best' model, save all model params/outputs and return in an additional data object/structure.   

### Describe alternatives you've considered, if relevant

_No response_

### Additional context

_No response_

COMMENT:
@cppt , here is the code:

```
import numpy as np
from sklearn.linear_model import enet_path

# Create some sample data
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3

# Compute the regularization path
alphas, coefs, intercepts = enet_path(X, y, verbose = 5)
# Print the alphas, coefficients, and intercepts for all scenarios tested
for alpha, coef, intercept in zip(alphas, coefs, intercepts):
    print("Alpha:", alpha)
    print("Coefficients:", coef)
    print("Intercept:", intercept)
    print()
```

Output:
(array([0., 0.]), 0.0, 0.0302, 1)
(array([0.        , 0.05715559]), 0.0, 0.0302, 2)
(array([0.        , 0.11667845]), 5.684341886080802e-14, 0.0302, 2)
(array([0.        , 0.17856487]), 0.0, 0.0302, 2)
(array([0.        , 0.24279889]), -5.684341886080802e-14, 0.0302, 2)
(array([0.        , 0.30935145]), 0.0, 0.0302, 2)
(array([0.01901234, 0.37443649]), 1.3194308507991082e-06, 0.0302, 4)
(array([0.06464451, 0.43584286]), 5.815761113581175e-06, 0.0302, 4)
(array([0.11104886, 0.49826743]), 1.3875526946094396e-05, 0.0302, 4)
(array([0.15812687, 0.56158046]), 1.6494158217028598e-06, 0.0302, 5)
(array([0.2057831 , 0.62564193]), 3.247510619530658e-06, 0.0302, 5)
(array([0.25390901, 0.69030648]), 5.998067450718736e-06, 0.0302, 5)
(array([0.30239422, 0.75542238]), 1.0576523891359102e-05, 0.0302, 5)
(array([0.35112521, 0.82083339]), 1.797996975483329e-05, 0.0302, 5)
(array([0.39998632, 0.88638024]), 2.9643879514651417e-05, 0.0302, 5)
(array([0.44886095, 0.95190213]), 4.75841175102687e-05, 0.0302, 5)
(array([0.4976327 , 1.01723829]), 7.456191761434638e-05, 0.0302, 5)
(array([0.54618652, 1.08222949]), 0.00011426530710423322, 0.0302, 5)
(array([0.59440985, 1.14671955]), 0.00017149506439295692, 0.0302, 5)
(array([0.64219371, 1.21055678]), 0.00025233719833295254, 0.0302, 5)
(array([0.68943375, 1.27359533]), 0.0003642978385300921, 0.0302, 5)
(array([0.73600503, 1.33570614]), 9.317236222727843e-05, 0.0302, 6)
(array([0.78185772, 1.39674312]), 0.0001400085866976042, 0.0302, 6)
(array([0.82688711, 1.45659175]), 0.00020630181253977753, 0.0302, 6)
(array([0.87101494, 1.51514063]), 0.0002981673163162668, 0.0302, 6)
(array([0.91417102, 1.5722888 ]), 0.00042285210944470464, 0.0302, 6)
(array([0.95629359, 1.6279462 ]), 0.0005886332188183019, 0.0302, 6)
(array([0.99732949, 1.68203387]), 0.0008046054945296532, 0.0302, 6)
(array([1.03718305, 1.73450754]), 0.0003165159688549579, 0.0302, 7)
(array([1.07590454, 1.78527184]), 0.0004431231468089436, 0.0302, 7)
(array([1.11342785, 1.83429845]), 0.0006085892242211344, 0.0302, 7)
(array([1.14973329, 1.88155252]), 0.0008200316172946032, 0.0302, 7)
(array([1.18480902, 1.92700936]), 0.0010844981319380054, 0.0302, 7)
(array([1.21865063, 1.97065383]), 0.001408358238293772, 0.0302, 7)
(array([1.25117521, 2.01252455]), 0.0007281750637666562, 0.0302, 8)
(array([1.28254042, 2.05254682]), 0.0009553156995636414, 0.0302, 8)
(array([1.31269417, 2.09076491]), 0.0012308004081234003, 0.0302, 8)
(array([1.34165629, 2.12719574]), 0.001556845843310839, 0.0302, 8)
(array([1.36945136, 2.16186239]), 0.0019345101170102907, 0.0302, 8)
(array([1.39599369, 2.19485863]), 0.0011828625463508047, 0.0302, 9)
(array([1.42152022, 2.22610224]), 0.0014705246886403955, 0.0302, 9)
(array([1.44597336, 2.25568279]), 0.0017984996816977628, 0.0302, 9)
(array([1.46939059, 2.28364133]), 0.002161982013863195, 0.0302, 9)
(array([1.49181143, 2.31002169]), 0.0025561466318571036, 0.0302, 9)
(array([1.51313001, 2.33495931]), 0.001755255011964607, 0.0302, 10)
(array([1.53365873, 2.35833888]), 0.002060662494283605, 0.0302, 10)
(array([1.55331617, 2.38028421]), 0.0023873322183831647, 0.0302, 10)
(array([1.57214462, 2.40084582]), 0.0027231035870052267, 0.0302, 10)
(array([1.59018678, 2.42007443]), 0.0030601661872324826, 0.0302, 10)
(array([1.60731468, 2.43812958]), 0.002267589904306533, 0.0302, 11)
(array([1.62389131, 2.45485819]), 0.0025147333974242514, 0.0302, 11)
(array([1.6398085 , 2.47040489]), 0.0027633870586996068, 0.0302, 11)
(array([1.65510415, 2.48482075]), 0.0029952686152476815, 0.0302, 11)
(array([1.66981648, 2.49815509]), 0.0032039541598951615, 0.0302, 11)
(array([1.68398224, 2.51045643]), 0.003383846876634067, 0.0302, 11)
(array([1.69763653, 2.52177235]), 0.003530214226813655, 0.0302, 11)
(array([1.7108127 , 2.53214943]), 0.003639320592169426, 0.0302, 11)
(array([1.72354233, 2.54163315]), 0.003708501889304827, 0.0302, 11)
(array([1.73585512, 2.55026788]), 0.0037361839696927746, 0.0302, 11)
(array([1.74777888, 2.5580968 ]), 0.003721852482371446, 0.0302, 11)
(array([1.75933949, 2.56516191]), 0.0036659837869166267, 0.0302, 11)
(array([1.77056088, 2.57150404]), 0.00356994707446745, 0.0302, 11)
(array([1.78146505, 2.57716285]), 0.0034358874668125594, 0.0302, 11)
(array([1.79227591, 2.58203659]), 0.003982404951348606, 0.0302, 10)
(array([1.80262024, 2.58643133]), 0.003807589819487589, 0.0302, 10)
(array([1.81289059, 2.59012413]), 0.004213254973232772, 0.0302, 9)
(array([1.82271506, 2.59341721]), 0.003930192435560365, 0.0302, 9)
(array([1.83247549, 2.59608888]), 0.004142907300813903, 0.0302, 8)
(array([1.84181918, 2.59842501]), 0.003749310136090145, 0.0302, 8)
(array([1.85130873, 2.60007179]), 0.004356432275689048, 0.0302, 6)
(array([1.86025481, 2.60154708]), 0.0039903360093003215, 0.0302, 6)
(array([1.86928474, 2.60244646]), 0.0043643070791796745, 0.0302, 4)
(array([1.87801167, 2.60307353]), 0.004425443659130224, 0.0302, 3)
(array([1.88645693, 2.60344448]), 0.004249947511216767, 0.0302, 2)
(array([1.89441145, 2.60373623]), 0.0033649108293953844, 0.0302, 2)
(array([1.90196687, 2.60391233]), 0.0020436509041052986, 0.0302, 2)
(array([1.90918874, 2.60395269]), 0.0004711652838800262, 0.0302, 2)
(array([1.91612453, 2.6038482 ]), 0.00167596941914816, 0.0302, 2)
(array([1.92280901, 2.6035971 ]), 0.004071920576677712, 0.0302, 2)
(array([1.92950366, 2.60303499]), 0.005798944142170193, 0.0302, 3)
(array([1.9365236 , 2.60195723]), 0.005998496484806548, 0.0302, 5)
(array([1.94326137, 2.60081402]), 0.006433019368176307, 0.0302, 5)
(array([1.95001382, 2.59941164]), 0.006279225706987468, 0.0302, 6)
(array([1.95646083, 2.59799461]), 0.006416734108870781, 0.0302, 6)
(array([1.96290002, 2.59636619]), 0.006047004658215727, 0.0302, 7)
(array([1.96901443, 2.59476707]), 0.006006448644048312, 0.0302, 7)
(array([1.97487238, 2.59316222]), 0.006104199110143593, 0.0302, 7)
(array([1.98050722, 2.59154059]), 0.006250992622957696, 0.0302, 7)
(array([1.98593567, 2.58990216]), 0.006404871518275179, 0.0302, 7)
(array([1.99116647, 2.58825181]), 0.006546459726492593, 0.0302, 7)
(array([1.9962046 , 2.58659636]), 0.006667379095167458, 0.0302, 7)
(array([2.00105333, 2.58494317]), 0.006764688637442617, 0.0302, 7)
(array([2.00571525, 2.58329945]), 0.006838155449508498, 0.0302, 7)
(array([2.01019279, 2.58167192]), 0.006888886693848573, 0.0302, 7)
(array([2.01448848, 2.58006668]), 0.006918629284542455, 0.0302, 7)
(array([2.01860507, 2.5784891 ]), 0.006929403744237117, 0.0302, 7)
(array([2.02254557, 2.57694391]), 0.006923308817433416, 0.0302, 7)
(array([2.0260774 , 2.57560473]), 0.007546682213824152, 0.0302, 6)
(array([2.02977259, 2.57406636]), 0.007255271415334885, 0.0302, 7)
(array([2.03303197, 2.57276514]), 0.007721195772496525, 0.0302, 6)
[1, 2, 2, 2, 2, 2, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 10, 10, 9, 9, 8, 8, 6, 6, 4, 3, 2, 2, 2, 2, 2, 2, 3, 5, 5, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 7, 6]
Alpha: 36.5
Coefficients: [0.         0.         0.         0.         0.         0.
 0.01901234 0.06464451 0.11104886 0.15812687 0.2057831  0.25390901
 0.30239422 0.35112521 0.39998632 0.44886095 0.4976327  0.54618652
 0.59440985 0.64219371 0.68943375 0.73600503 0.78185772 0.82688711
 0.87101494 0.91417102 0.95629359 0.99732949 1.03718305 1.07590454
 1.11342785 1.14973329 1.18480902 1.21865063 1.25117521 1.28254042
 1.31269417 1.34165629 1.36945136 1.39599369 1.42152022 1.44597336
 1.46939059 1.49181143 1.51313001 1.53365873 1.55331617 1.57214462
 1.59018678 1.60731468 1.62389131 1.6398085  1.65510415 1.66981648
 1.68398224 1.69763653 1.7108127  1.72354233 1.73585512 1.74777888
 1.75933949 1.77056088 1.78146505 1.79227591 1.80262024 1.81289059
 1.82271506 1.83247549 1.84181918 1.85130873 1.86025481 1.86928474
 1.87801167 1.88645693 1.89441145 1.90196687 1.90918874 1.91612453
 1.92280901 1.92950366 1.9365236  1.94326137 1.95001382 1.95646083
 1.96290002 1.96901443 1.97487238 1.98050722 1.98593567 1.99116647
 1.9962046  2.00105333 2.00571525 2.01019279 2.01448848 2.01860507
 2.02254557 2.0260774  2.02977259 2.03303197]
Intercept: 0.0

Alpha: 34.04002216123752
Coefficients: [0.         0.05715559 0.11667845 0.17856487 0.24279889 0.30935145
 0.37443649 0.43584286 0.49826743 0.56158046 0.62564193 0.69030648
 0.75542238 0.82083339 0.88638024 0.95190213 1.01723829 1.08222949
 1.14671955 1.21055678 1.27359533 1.33570614 1.39674312 1.45659175
 1.51514063 1.5722888  1.6279462  1.68203387 1.73450754 1.78527184
 1.83429845 1.88155252 1.92700936 1.97065383 2.01252455 2.05254682
 2.09076491 2.12719574 2.16186239 2.19485863 2.22610224 2.25568279
 2.28364133 2.31002169 2.33495931 2.35833888 2.38028421 2.40084582
 2.42007443 2.43812958 2.45485819 2.47040489 2.48482075 2.49815509
 2.51045643 2.52177235 2.53214943 2.54163315 2.55026788 2.5580968
 2.56516191 2.57150404 2.57716285 2.58203659 2.58643133 2.59012413
 2.59341721 2.59608888 2.59842501 2.60007179 2.60154708 2.60244646
 2.60307353 2.60344448 2.60373623 2.60391233 2.60395269 2.6038482
 2.6035971  2.60303499 2.60195723 2.60081402 2.59941164 2.59799461
 2.59636619 2.59476707 2.59316222 2.59154059 2.58990216 2.58825181
 2.58659636 2.58494317 2.58329945 2.58167192 2.58006668 2.5784891
 2.57694391 2.57560473 2.57406636 2.57276514]
Intercept: 0.0

Labels: New Feature