Why doesn't GridSearchCV have best_estimator_ even after fitting?

Question

I am learning about multiclass classification using scikit learn. My goal is to develop a code which tries to include all the possible metrics needed to evaluate the classification. This is my code:

from sklearn.model_selection import GridSearchCV
from sklearn.metrics import make_scorer, precision_score, recall_score, f1_score

param_grid = [
    {'estimator__randomforestclassifier__n_estimators': [3, 10], 'estimator__randomforestclassifier__max_features': [2]},
#    {'estimator__randomforestclassifier__bootstrap': [False], 'estimator__randomforestclassifier__n_estimators': [3, 10], 'estimator__randomforestclassifier__max_features': [2, 3, 4]}
]

rf_classifier = OneVsRestClassifier(
    make_pipeline(RandomForestClassifier(random_state=42))
)

scoring = {'accuracy': make_scorer(accuracy_score),
           'precision_macro': make_scorer(precision_score, average = 'macro'),
           'recall_macro': make_scorer(recall_score, average = 'macro'),
           'f1_macro': make_scorer(f1_score, average = 'macro'),
           'precision_micro': make_scorer(precision_score, average = 'micro'),
           'recall_micro': make_scorer(recall_score, average = 'micro'),
           'f1_micro': make_scorer(f1_score, average = 'micro'),
           'f1_weighted': make_scorer(f1_score, average = 'weighted')}

grid_search = GridSearchCV(rf_classifier, param_grid=param_grid, cv=2, 
scoring=scoring, refit=False)
grid_search.fit(X_train_prepared, y_train)

However when I try to find out the best estimator, I get the following error message:

print(grid_search.best_params_)
print(grid_search.best_estimator_)

AttributeError: 'GridSearchCV' object has no attribute 'best_params_'

Question: How is it possible that even after fitting the model I do not get the best estimator? I noticed that if I set refit="some_of_the_metrics", I get an estimator but I do not understand why I should use it since it would fit the method to optimize a metric instead of all of them. Therefore, how can I get the best estimator for all the scores? And what is the the point of refit?

Note: I tried to read the documentation but it still does not make sense to me.