title: How does LassoCV in scikit-learn partition data?
body: <p>I am performing linear regression using the Lasso method in sklearn. </p>
  
  <p>According to their guidance, and that which I have seen elsewhere, instead of simply conducting cross validation on all of the training data it is advised to split it up into more traditional training set / validation set partitions. </p>
  
  <p>The Lasso is thus trained on the training set and then the hyperparameter alpha is tuned on the basis of results from cross validation of the validation set. Finally, the accepted model is used on the test set to give a realistic view oh how it will perform in reality. Seperating the concerns out here is a preventative measure against overfitting.</p>
  
  <p><strong>Actual Question</strong></p>
  
  <p>Does Lasso CV conform to the above protocol or does it just somehow train the model paramaters and hyperparameters on the same data and/or during the same rounds of CV?</p>
  
  <p>Thanks.</p>