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:
If I recall properly, this is not a bug on our side but from Jupyter.
I can even see the output while killing the kernel:

```shell
[I 17:45:32.952 NotebookApp] Kernel shutdown: 0cc5ae70-c404-4f10-92fb-f39263d60d41
[CV 2/5; 1/30] START max_depth=20, n_estimators=10..............................
[CV 2/5; 1/30] END max_depth=20, n_estimators=10;, score=0.967 total time=   0.0s
[CV 5/5; 2/30] START max_depth=20, n_estimators=20..............................
[CV 5/5; 2/30] END max_depth=20, n_estimators=20;, score=1.000 total time=   0.0s
[CV 1/5; 5/30] START max_depth=20, n_estimators=50..............................
[CV 1/5; 5/30] END max_depth=20, n_estimators=50;, score=0.967 total time=   0.0s
[CV 2/5; 5/30] START max_depth=20, n_estimators=50..............................
[CV 2/5; 5/30] END max_depth=20, n_estimators=50;, score=0.967 total time=   0.0s
[CV 1/5; 9/30] START max_depth=50, n_estimators=30..............................
[CV 1/5; 9/30] END max_depth=50, n_estimators=30;, score=0.967 total time=   0.0s
[CV 2/5; 9/30] START max_depth=50, n_estimators=30..............................
[CV 2/5; 9/30] END max_depth=50, n_estimators=30;, score=0.967 total time=   0.0s
[CV 1/5; 13/30] START max_depth=60, n_estimators=10.............................
[CV 1/5; 13/30] END max_depth=60, n_estimators=10;, score=0.967 total time=   0.0s
[CV 2/5; 13/30] START max_depth=60, n_estimators=10.............................
[CV 2/5; 13/30] END max_depth=60, n_estimators=10;, score=0.967 total time=   0.0s
[CV 3/5; 13/30] START max_depth=60, n_estimators=10.............................
[CV 3/5; 13/30] END max_depth=60, n_estimators=10;, score=0.900 total time=   0.0s
[CV 4/5; 13/30] START max_depth=60, n_estimators=10.............................
[CV 4/5; 13/30] END max_depth=60, n_estimators=10;, score=0.967 total time=   0.0s
[CV 2/5; 16/30] START max_depth=60, n_estimators=40.............................
[CV 2/5; 16/30] END max_depth=60, n_estimators=40;, score=0.967 total time=   0.0s
[CV 3/5; 16/30] START max_depth=60, n_estimators=40.............................
[CV 3/5; 16/30] END max_depth=60, n_estimators=40;, score=0.933 total time=   0.0s
[CV 4/5; 16/30] START max_depth=60, n_estimators=40.............................
[CV 4/5; 16/30] END max_depth=60, n_estimators=40;, score=0.900 total time=   0.0s
[CV 5/5; 16/30] START max_depth=60, n_estimators=40.............................
[CV 5/5; 16/30] END max_depth=60, n_estimators=40;, score=1.000 total time=   0.0s
[CV 4/5; 22/30] START max_depth=70, n_estimators=40.............................
[CV 4/5; 22/30] END max_depth=70, n_estimators=40;, score=0.867 total time=   0.0s
[CV 5/5; 22/30] START max_depth=70, n_estimators=40.............................
[CV 5/5; 22/30] END max_depth=70, n_estimators=40;, score=1.000 total time=   0.0s
[CV 1/5; 23/30] START max_depth=70, n_estimators=50.............................
[CV 1/5; 23/30] END max_depth=70, n_estimators=50;, score=0.967 total time=   0.0s
[CV 2/5; 23/30] START max_depth=70, n_estimators=50.............................
[CV 2/5; 23/30] END max_depth=70, n_estimators=50;, score=0.967 total time=   0.0s
[CV 1/5; 29/30] START max_depth=80, n_estimators=50.............................
[CV 1/5; 29/30] END max_depth=80, n_estimators=50;, score=0.967 total time=   0.0s
[CV 1/5; 1/30] START max_depth=20, n_estimators=10..............................
[CV 1/5; 1/30] END max_depth=20, n_estimators=10;, score=0.967 total time=   0.0s
[CV 3/5; 2/30] START max_depth=20, n_estimators=20..............................
[CV 3/5; 2/30] END max_depth=20, n_estimators=20;, score=0.933 total time=   0.0s
[CV 1/5; 4/30] START max_depth=20, n_estimators=40..............................
[CV 1/5; 4/30] END max_depth=20, n_estimators=40;, score=0.967 total time=   0.0s
[CV 4/5; 6/30] START max_depth=20, n_estimators=60..............................
[CV 4/5; 6/30] END max_depth=20, n_estimators=60;, score=0.933 total time=   0.0s
[CV 5/5; 6/30] START max_depth=20, n_estimators=60..............................
[CV 5/5; 6/30] END max_depth=20, n_estimators=60;, score=1.000 total time=   0.0s
```

Labels: Bug, module:model_selection, Needs Investigation