Verbosity option is not working in GridSearchCV (Jupyter notebook)

### Describe the bug

So this issue has been addressed before [here ](https://github.com/scikit-learn/scikit-learn/issues/22291) by [darrencl](https://github.com/darrencl), but the user didn't follow up with [lesteve](https://github.com/lesteve) response.

The problem is that GridSearchCV doesn't show the elapsed time periodically, or any log, I am set`n_jobs = -1`, and `verbose = 1`. I tried setting `n_jobs` to other values, the same with `verbose`, but nothing happened.
Note that this didn't happen until I updated scikit-learn from version 0.22.1 to 1.0.2.

[lesteve](https://github.com/lesteve) in his response assumed that this problem is due to `ipykernel <6`, which is not the case with me.

### Steps/Code to Reproduce

```
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn import datasets

iris = datasets.load_iris()
params = {'n_estimators':[10,20,30,40,50,60],
          'max_depth':[20,50,60,70,80]}
grid_obj = GridSearchCV(estimator=RandomForestClassifier(), param_grid=params, n_jobs=-1, verbose=1, cv=5)
grid_obj.fit(iris.data, iris.target)
```

### Expected Results

This is the output when using version 0.22.1
![image](https://user-images.githubusercontent.com/17684093/158401635-31755363-2108-41e7-8f7f-08985abd03b8.png)


### Actual Results

```Fitting 5 folds for each of 30 candidates, totalling 150 fits```

### Versions

```shell
System:
    python: 3.8.5 (default, Sep  3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)]
executable: D:\Programs\ApplicationsSetup\anaconda3\python.exe
   machine: Windows-10-10.0.19041-SP0

Python dependencies:
          pip: 21.2.2
   setuptools: 58.0.4
      sklearn: 1.0.2
        numpy: 1.19.2
        scipy: 1.6.2
       Cython: 0.29.25
       pandas: 1.4.1
   matplotlib: 3.5.1
       joblib: 1.1.0
threadpoolctl: 2.2.0


!jupyter --version
Selected Jupyter core packages...
IPython          : 7.31.1
ipykernel        : 6.4.1
ipywidgets       : 7.6.5
jupyter_client   : 6.1.12
jupyter_core     : 4.9.1
jupyter_server   : 1.13.5
jupyterlab       : 3.2.9
nbclient         : 0.5.11
nbconvert        : 6.1.0
nbformat         : 5.1.3
notebook         : 6.4.9
qtconsole        : 5.2.2
traitlets        : 5.1.1
```

COMMENT:
Reading the top post in more details:
- it seems like you need `verbose > 1` if you want more details about the cross-validation progress. Maybe you did not need it in scikit-learn 0.22.1 but you definitely need it now. You can double-check with `n_jobs=1` and `verbose=10`, you should definitely see some output about the cross-validation progress
- there is something fishy with subprocess output capture (maybe only for loky backend not 100% sure). The first time I execute the cell, I don't get any output about CV progress, the second time I do  ...

A screenshot with a slight variation (first `.fit` does not show any progress, second `.fit` does):
![image](https://user-images.githubusercontent.com/1680079/158440240-35fd52b3-16d8-4afb-82c7-3425ee882191.png)

When I use the multiprocessing backend I do get the output consistently so maybe a weird interaction between loky and ipykernel sub-process output capturing?

```py
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn import datasets

from joblib import parallel_backend

iris = datasets.load_iris()
params = {'n_estimators':[10,20,30,40,50,60],
          'max_depth':[20,50,60,70,80]}

grid_obj = GridSearchCV(estimator=RandomForestClassifier(), param_grid=params, n_jobs=-1, verbose=10, cv=5)

with parallel_backend('multiprocessing'):
    grid_obj.fit(iris.data, iris.target)
```

Labels: Bug, module:model_selection, Needs Investigation