Evaluating prediction performance I have 2 datasets, I am creating models on the 1. one (tree,forest,lasso,stepwise OLS, all cross-validated) and then I have to predict the 'Age' variable I am training the models on for the 2. dataset (not part of the 2. set). What metrics could I use to choose a model that is the best? Do I only look at the MSE, AIC, BIC, R square on the first dataset or should I look at the performance of the prediction as  well? If that's possible at all?

As user2974951 writes, do not look at in-sample performance, which is a notoriously poor indicator of true predictive performance.
There are many different point forecast accuracy measures; here is a list and discussion. Be aware that your models will likely try to give you unbiased expectation forecasts, which minimize the MSE (and variants thereof). If you want to minimize the MAPE, you should tell your models about this, because you want different forecasts then!
