GridSearchCV does not return trained estimator for each split vs cross_validate which does

### Describe the workflow you want to enable

GridSearchCV does not return trained estimator for each split vs cross_validate which does have trained estimators for each split. Instead GridSearchCV returns best_estimator_ which is usually being refit on X and thus can not be used on X to predict test data. cross_validate returns trained estimators which can be used for prediction on test data.

For me to effectively use result of GridseachCV for prediction on test data I need to retrain the estimators on my train/test splits using best params. This takes additional time and was done once already when GridsearchCV was fitted.

### Describe your proposed solution

Return trained estimators for each test split

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

rewrite GridSearchCV or retrain estimators again after GridsearchCV

### Additional context

_No response_

COMMENT:
@kabartay, I did a quick experiment to compare the accuracy of refit on all data and 5 Fold Ensemble, and the results are in the table below. Here, "Refit on All Data" is the test accuracy of the `best_estimator_` returned by GridSearchCV. "5 Fold Ensemble Soft/Hard" is the accuracy of the ensemble predictors consisting of 5 estimators from 5-fold cross-validation, trained with `best_params_`.

Overall, by using ensemble, there is a notable increase in the accuracy of `DecisionTreeClassifier` (it becomes Random Forest), while the accuracy of other estimators has not changed significantly.
| Estimator              | Dataset              | Refit on All Data | 5 Fold Ensemble Soft | 5 Fold Ensemble Hard |
| ---------------------- | -------------------- | ----------------- | -------------------- | -------------------- |
| LogisticRegression     | make\_classification | 92.80%            | 92.80%               | 92.80%               |
| LogisticRegression     | make\_blobs          | 80.20%            | 80.20%               | 80.00%               |
| LogisticRegression     | iris 2 feature       | 86.84%            | 86.84%               | 86.84%               |
| LogisticRegression     | digits               | 97.33%            | 97.33%               | 97.33%               |
| LogisticRegression     | wine                 | 100.00%           | 100.00%              | 100.00%              |
| LogisticRegression     | breast\_cancer       | 96.50%            | 96.50%               | 96.50%               |
| **LogisticRegression**     |**average**             | **92.28%**            |**92.28%**               | **92.25%**               |
| Linear SVC             | make\_classification | 92.40%            | 92.80%               | 92.40%               |
| Linear SVC             | make\_blobs          | 81.00%            | 80.20%               | 81.00%               |
| Linear SVC             | iris 2 feature       | 84.21%            | 84.21%               | 84.21%               |
| Linear SVC             | digits               | 98.22%            | 98.67%               | 98.00%               |
| Linear SVC             | wine                 | 100.00%           | 97.78%               | 100.00%              |
| Linear SVC             | breast\_cancer       | 96.50%            | 96.50%               | 95.80%               |
| **Linear SVC**             | **average**              | **92.06%**            | **91.69%**               | **91.90%**               |
| KNeighborsClassifier   | make\_classification | 92.80%            | 92.80%               | 92.80%               |
| KNeighborsClassifier   | make\_blobs          | 80.00%            | 80.80%               | 80.60%               |
| KNeighborsClassifier   | iris 2 feature       | 78.95%            | 78.95%               | 78.95%               |
| KNeighborsClassifier   | digits               | 98.67%            | 98.67%               | 98.67%               |
| KNeighborsClassifier   | wine                 | 68.89%            | 71.11%               | 68.89%               |
| KNeighborsClassifier   | breast\_cancer       | 97.20%            | 97.20%               | 97.90%               |
| **KNeighborsClassifier**   | **average**              | **86.08%**           | **86.59%**              | **86.30%**               |
| RBF SVC                | make\_classification | 94.40%            | 94.80%               | 94.40%               |
| RBF SVC                | make\_blobs          | 80.80%            | 80.80%               | 81.00%               |
| RBF SVC                | iris 2 feature       | 84.21%            | 84.21%               | 84.21%               |
| RBF SVC                | digits               | 98.89%            | 98.89%               | 99.11%               |
| RBF SVC                | wine                 | 73.33%            | 75.56%               | 73.33%               |
| RBF SVC                | breast\_cancer       | 95.11%            | 96.50%               | 95.11%               |
| **RBF SVC**                | **average**              |**87.79%**            | **88.46%**               | **87.86%**               |
| DecisionTreeClassifier | make\_classification | 93.60%            | 94.00%               | 94.00%               |
| DecisionTreeClassifier | make\_blobs          | 79.40%            | 80.80%               | 81.00%               |
| DecisionTreeClassifier | iris 2 feature       | 76.32%            | 78.95%               | 78.95%               |
| DecisionTreeClassifier | digits               | 86.44%            | 91.33%               | 91.33%               |
| DecisionTreeClassifier | wine                 | 95.56%            | 93.33%               | 93.33%               |
| DecisionTreeClassifier | breast\_cancer       | 95.80%            | 95.80%               | 95.80%               |
| **DecisionTreeClassifier** | **average**              | **87.85%**            | **89.04%**              | **89.07%**               |
| **Average Overall**        | **\-**                   | **89.21%**            | **89.61%**               | **89.48%**               |

*datasets are: `datasets.make_classification()`, `datasets.make_blobs()`, `datasets.load_iris()`, `datasets.load_digits()`, `datasets.load_wine()`, `datasets.load_breast_cancer()`

Labels: New Feature, module:model_selection