Training a model with Cross-Validation

I'm training a model with CV and then I'm testing the predictions on a new test set.

Am I doing the right thing or is it necessary to test the predictions on a new dataset using Cross-Validation?

Thank you!

• You have a wrong understanding of cross-validation. To use CV, you better separate another set from the original training set as the validation set. This is where the cross-validation is going to work. Apr 30 at 1:08

Everybody understands how to perform $$k$$-fold cross-validation but there is often quite a lot of confusion about where/how to use it. So thanks for this good question :)
• Of course training is performed during cross-validation, but it is performed $$k$$ times and therefore there are $$k$$ models produced during the process. Each of these models is meant to be applied only to its corresponding test set, and in general it would be a mistake to use one of these models after the CV process (it would be an even bigger mistake to select the best of these $$k$$ models). If a final model is needed, it should be trained on the whole training set independently from the CV process.