I am confused about how exactly cvglmnet calculates the "optimal" hyperparameters (specifically, lambda.min and lambda.1se).
As far as I understand, k-fold cross validation in itself doesn't give us an "optimal hyperparameter", but rather an estimate of the model's overall performance on other datasets. And cross-validation is what the documentation says that cvglmnet does.
However, isn't cvglmnet providing us with a lambda.min and lambda.1se a form of hyperparameter search? So would the results of running cvglmnet effectively be similar to nested cross validation?
Any thoughts are appreciated!