Can any "model" be cross validated? Suppose you have a dataset that has heights of people and whether they play basketball professionally. You have a theory that anyone taller than 200 cm plays professional basketball and everyone shorter does not. This is your "decision rule" (i.e. model).
If you took 20% of this data and tested this theory - measured how well this decision rule predicted if someone plays professional basketball: is this a statistically valid approach to test how well this rule might "generalize" to unseen data? Then repeat this several times on random 20% subsets of the data?
Does this sound reasonable?

Your example is not a model, but a decision rule. A model would predict a probability
$$P(basketball\mid height)$$
Modeling this probability could be done parametrically (e.g. logistic regression) or non-parametrically (e.g. kNN, or kernel density estimation as the R function cdplot does). A decision rule is then derived from the model on basis of a trianing set, and this decision rule can be evaluated by cross-validation.
That textbooks discussing classical statistical models (e.g. logistic regression,, which is a special case of "generalized linear models") rarely suggest cross-validation for model validation might be a historical legacy: The theory of linear models predates the wide-spread use of cross-validation. Actually, all textbooks that I have read so far about linear models only suggest a resubstitution of the training set into the model for evaluation. This does not mean, howver, that cross-validation cannot be used and it should provide a less biased method for evaluating a model.
