Cross validation Vs. Train Validate Test cont'd
The answer in the linked question says to use approach 2. Specifically:


Run cross-validation on the train set separately for each choice of hyper-parameter values.
On each run, average the score across the k folds to produce a final cross-validation score. 
Choose the hyper-parameters that give us the best cross-validation score.
Retrain a model with those hyper-parameters on the entire training set.
Test this model on the test set, which has not been used at all until now.