title: GridSearchCV.best_score not same as cross_val_score(GridSearchCV.best_estimator_)
body: <p>Consider the following gridsearch :<br>
  <code>grid = GridSearchCV(clf, parameters, n_jobs =-1, iid=True, cv =5)</code><br>
  <code>grid_fit = grid.fit(X_train1, y_train1)</code></p>
  
  <p>According to Sklearn's ressource, <code>grid_fit.best_score_</code>
  returns <em>The mean cross-validated score of the best_estimator</em> . </p>
  
  <p>To me that would mean that the average of : </p>
  
  <p><code>cross_val_score(grid_fit.best_estimator_, X_train1, y_train1, cv=5)</code>  </p>
  
  <p>should be exactly the same as:  </p>
  
  <p><code>grid_fit.best_score_</code>.</p>
  
  <p>However I am getting a 10% difference between the two numbers. What am I missing ?</p>
  
  <p>I am using the gridsearch on proprietary data so I am hoping somebody has run into something similar in the past and can guide me without a fully reproducible example. I will try to reproduce this with the Iris dataset if it's not clear enough...  </p>