In many machine learning research papers, the authors state, "We performed k-fold cross validation and report the average performance metrics on the test set across all folds". No mention of model selection or hyperparameter tuning.
To my understanding, this would be ok (not lead to a biased metric) if there was no hyperparameter tuning or model selection done as part of the cross validation. In other words, they randomly/manually chose hyperparameters, performed k-fold cross validation once, and reported average test set performance metrics.
However, I find it hard to believe that the authors of these papers are not hyperparameter tuning. Are they simply excluding this and claiming that hyperparameters that they chose manually on the first go work well, or is hyperparameter tuning on a k-fold cross validation to find the best hyperparameters and reporting the biased average test set performance metrics so common that it is just accepted now?