How to evaluate the "betterness" of competitive good models? How to evaluate the "betterness" of competitive good models?

Lets say I could get good models (> 90% prediction rate) with e.g.:


LinearSVC
F-test based sklearn.feature_selection.f_regression
mutual info -based sklearn.feature_selection.mutual_info_regression


But since these treat e.g. the independence/dependence of features differently, particularly e.g. LinearSVC assumes "relatively independent" features, where as mutual info particularly measure dependence between variables, then

How can I compare these models to each other?

Tests? Knowledge of the data a priori? Something else?
The typical approach would be to compare (cross-)validation performance of the models, at least if accuracy is the selection criterion. 

Other criteria could be


simplicity/ease to interpret 
how easy the models could be implemented and/or updated in a productive environment