Cross-validation is indeed typically used in the context of classification, since it's a method to measure accuracy on "unseen" data without having to explicitly set aside training data.
However, if you indeed want to compare the "accuracy" of different clustering methods, Tibshirani described an approach, where you essentially compare the clustering obtained just on the test set with the "closest" cluster derived from the training set. You can find more detail about this e.g. at https://stats.stackexchange.com/questions/87098/can-you-compare-different-clustering-methods-on-a-dataset-with-no-ground-truth-b
There is no "built-in" crossvalidation in Matlab stats functions, but you can use crossvalind to program it out yourself, see example near the bottom of https://www.mathworks.com/help/bioinfo/ref/crossvalind.html.
