+1.

And in particular, I think we should somehow explain that if you optimize the hyperparameters over a validation set, then the validation error is itself biased, i.e., it may not be a good estimate of the true generalization error of the model. More importantly, this also holds when optimizing the parameters using cross-validation. The CV error is biased in the same way. 

Too often I indeed see papers reporting CV errors thinking it is an unbiased estimate of the generalization error, but as soon as you repeatedly try to minimize the CV error (e.g., using grid search), then you can no longer use it for assessing the true performance of your model.  

This may be a good opportunity for describing protocols for both model selection and model assessment (I have in mind the train/valid/test protocol, or nested cross-validation).