I just learned of nested cross-validation and wanted to understand how my current approach is worse/ok.
Currently I would:
Divide the data into a train/test set (80/20ish).
Use k-fold cross-validation on the training set to tune my model.
Refit the model on the training set and stop touching it. Get the score of the test set as my sense of how well the model will perform. Done.
What is the comparative advantage of using a nested cross-validation approach instead? Is it simply that I get to use my full dataset with nested cv (and maybe I get a distribution estimate instead of a point one)?