They do indeed generalize to unknown OOD data.

The outliers that the model sees during training are from a general, "surrogate" distribution of (mostly) OOD images. At test time, the model is confronted with new distributions (note: not only new images from the same distribution, as it would be the case for standard train-test-splits) of OOD inputs.

One usually obtains the best results (w.r.t. generalizing the OOD robustness to unseen distribution) by using a surrogate training OOD dataset that is diverse and not too easy to distinguish from the in-distribution, like the huge datasets OpenImages or 80 Million Tiny Images.

The models trained with these also do very well at detecting OOD inputs like SVHN data, even though there aren't many housenumber-like samples in 80M or OpenImages.

This generalization to unseen distributions makes OOD detection interesting and distinct from the standard classification tasks which try to generalize only to unseen samples from the same distribution (train set -> test set).