title: Can GridsearchSV include a randomization for train_test_split
body: <p>With Sklearn there is GridSearchCV to test multiple variables for classifier functions, for example:</p>
  
  <pre><code>parameters = {
    'learning_rate': [0.001,0.005,0.003],
    'n_estimators': [300,800,1200],
    'criterion': ['friedman_mse','mse','mae'],
    'verbose':[1],
    'loss' : ['deviance','exponential'],
    'random_state':[0]
    }
  
  GBC = GradientBoostingClassifier()
  grid = GridSearchCV(GBC, parameters)
  grid.fit(X,y )   # X = data,  y = result
  best_est = grid.best_estimator_
  print(best_est)
  
  predictions = best_est.predict(T) # T contains data to apply it on.
  </code></pre>
  
  <p>But what if one would like to do cross validation?  E.g. in a similar manner to <code>train_test_split</code>:</p>
  
  <pre><code>  X_train, X_test, y_train, y_test = train_test_split(X, y,  random_state=41)
  </code></pre>
  
  <p>Here we have a <code>random_state</code> (which might have a big impact).
  Is it possible to include the GridSearchCV with an array of a few random numbers to make sure it works optimally with 'most' random state's of some data's train/test split?</p>
  
  <p>For the record, I know this isn't inside GridSearchCV (or as far as i know), I'm asking here what such a method might look like. Perhaps there is some clever way to do this?</p>