Recently I have come across a paper that proposes using a k-NN classifier on an specific dataset. The authors used all the data samples available to perform k-fold cross validation for different k values and report cross validation results of the best hyperparameter configuration.
To my knowledge, this result is biased, and they should have retained a separate test set to obtain an accuracy estimate on samples not used to perform hyperparameter optimization.
Am I right? Can you provide some references (preferably research papers) that describe this misuse of cross validation?