Cross-indexing is a method that helps pick the best model and estimate its performance accurately. It prevents bias when comparing many models using cross-validation. A new version of cross-indexing is introduced in this paper, which can be used for any model selection problem with lots of options. The researchers modified the method to work with different model structures and hyperparameter values. They tested the new method in three competitions and found it to be very promising.