Before revision:  Selecting a model with the highest corrected Sharpe  selects the model with the highest expected out-of-sample Sharpe  in the same way as selection by Akaike Information Criterion does for the log-likelihood as measure of fit.
After revision:  Selecting a model with the highest corrected Sharpe ratio selects the model with the highest expected out-of-sample Sharpe in the same way as selection by Akaike Information Criterion does for the log-likelihood as measure of fit.