In this famous paper, Bailey and De Prado discard Cross Validation as tool to check for Backtest overfitting, on the ground that it is just an holdout method:
... If we apply the holdout method enough times (say 20 times for a 95% confidence level), false positives are no longer unlikely: They are expected. The more times we apply holdout, the more likely an invalid strategy will pass the test, which will then be published as a single-trial outcome ...
But publishing the results as a single-trial outcome is a misuse of Cross Validation. One should publish the average OOS performance of the K trials. So Bailey and De Prado don't have a point there. Cross Validation does solve the problem of backtest overfitting.
Am I missing something?