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?

The point of having a held-out test set is that your machine learning algorithm learned the rules from the data, so you cannot validate them on the same dataset, because you’d risk overfitting. If you derive your rule using exploratory data analysis or some kind of algorithm then the proper way would be to derive the rule using only training data and validate it using a test set. On another hand, if you made the rule a priori of looking at the data, just test it on the full dataset.
Moreover, your rule sounds like a hypothesis to be tested, if yes, we go into the realm of statistics and hypothesis-testing, rather than building predictive models. If that’s the case, I’d recommend you consult a statistics handbook beforehand, because statistics often use their methodologies, different from machine learning. For example, if you tested multiple rules like this (“let’s try 20%”, “ok, now 22%”, …), this is a multiple testing problem, and you risk making false discoveries. You would “overfit”, but cross-validation would not help here.
