RidgeCV replacement for normalize=True gives different results

### Describe the bug

Running RidgeCV with `normalize=True` results in a Future Warning that provides the new preferred method for normalizing prior to fitting a regularized regression:
FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
```python
from sklearn.pipeline import make_pipeline

model = make_pipeline(StandardScaler(with_mean=False), _RidgeGCV())
```

However, when I try to implement this, I get very different results than with normalize=True, making me think perhaps I didn't know what normalize was doing in the first place.

### Steps/Code to Reproduce

```python
from sklearn.datasets import make_regression
from sklearn import linear_model
from sklearn.pipeline import make_pipeline

X, y, w = make_regression(
    n_samples=10, n_features=10, coef=True, random_state=1, bias=3.5
)

reg = linear_model.RidgeCV(normalize=True)

reg.fit(X, y)
print('old method: ', reg.coef_, reg.intercept_, reg.alpha_, reg.predict(X))

model = make_pipeline(StandardScaler(with_mean=False), linear_model.RidgeCV())
model.fit(X, y)
print('new method: ',model['ridgecv'].coef_, model['ridgecv'].intercept_, model['ridgecv'].alpha_, model.predict(X))
```

### Expected Results

I would expect these to have the same result.

### Actual Results

```
old method:  [ 31.71587415 -22.24842109  16.39309719  21.21120971   6.0422928
  55.09416648  12.35520845 -31.34002266  41.30725923  71.19405147] 20.03577681177929 0.1 [  25.21745712   53.95958306  -68.09400539 -201.61697665   48.18804729
   -2.41519428   30.21811352  -25.23316533  108.24806911   27.85799418]
```
```
new method:  [ 64.42944029 -12.21304369  20.65494721  18.33833625   5.31778008
  50.76979275   9.707485   -16.44095044  51.70245172  23.1077787 ] 30.278259703366963 0.1 [  42.1051797    59.36215179  -82.18834535 -215.193882     55.814325
   -1.73335152   30.27829894  -40.63725969  108.43678068   40.08602508]
```
### Versions

```shell
System:
    python: 3.7.9 (default, Aug 31 2020, 12:42:55)  [GCC 7.3.0]
executable: /home1/djhalp/.conda/envs/cml/bin/python
   machine: Linux-3.10.0-693.17.1.el7.x86_64-x86_64-with-centos-7.4.1708-Core

Python dependencies:
          pip: 21.2.2
   setuptools: 58.0.4
      sklearn: 1.0.2
        numpy: 1.20.3
        scipy: 1.7.1
       Cython: 0.29.23
       pandas: 1.3.5
   matplotlib: 3.5.0
       joblib: 1.1.0
threadpoolctl: 2.2.0

Built with OpenMP: True
```

COMMENT:
The warning seems to be missing ``"Set parameter alphas to: original_alphas * n_samples. "`` which is intended to be displayed for RidgeCV, see [here](https://github.com/scikit-learn/scikit-learn/blob/0a01f90b9258402f962ed0c1d64670cd22a6d6cc/sklearn/linear_model/_base.py#L128).
So in your case you should set alphas=[1,10,100] in the pipeline.

The issue is that this message is not raised by RidgeCV but by _RidgeGCV which is ignored by this part of the warning. And actually this part of the warning seems to be unreachable code and is not tested anywhere.
We need to add _RidgeGCV to the list of estimators that trigger this part of the warning and add tests that actually check that all parts of the warning are displayed.

Labels: Bug, module:linear_model