I am a little confused when it comes to gridsearch and fitting the final model. I split the in 2: training and testing. The testing set is only used for final evaluation. I perform grid search only using the training data. 

Say one has done a grid search over several hyperparameters using cross-validation. The grid search gives the best combination of the hyperparameters. Next step is to train the model, and this is where I am confused. I see 2 possibilities:

1) Don't train the model. Use the parameters from the best model from the grid search. 

or

2) Don't use the parameters from the best model from the grid search. Train the model on the full training set with the best hyperparameter combination from the grid search.

What is the correct approach, 1 or 2?