Suppose I trained several models on training set, choose best one using cross validation set and measured performance on test set. So now I have one final best model. Should I retrain it on my all available data or ship solution trained only on training set? If latter, then why?
UPDATE: As @P.Windridge noted, shipping a retrained model basically means shipping a model without validation. But we can report test set performance and after that retrain the model on complete data righteously expecting the performance to be better - because we use our best model plus more data. What problems can arise from such methodology?