I understand that for small sample sizes its best not to opt for the traditional hold out validation but rather use the entire dataset and perform a k-fold cross-validation. However in my case i want to find the best hyper-parameters and select the best model, i understand that in this case a nested cross validation may be the better option.
If I understand the method of nested cross validation, i.e. take your entire dataset and split it into 2 folds. You then take each fold and perform a k-fold cross validation to determine best hyper-parametes on one fold. And for the other fold you perform a k-fold cross validation to determine the best model. Is my understanding of cross validation correct and if so how is this any different to hold one validation?