We provide background on differences between traditional and machine learning modeling. We then discuss how these differences impact the different validation needs of traditional and machine learning OPC compact models. We then provide multiple diverse examples of how machine learning OPC compact validation modeling can be appropriately validated both for modeling-specific production requirements such as model signal/contour accuracy, predictiveness, coverage and stability; and also general OPC mask synthesis requirements such as OPC/ILT stability, convergence, etc. Finally we conclude with thoughts on how machine learning modeling methods and their required validation methods are likely to evolve for future technology nodes.