Why a model that's "too good" on the data it trained on is a red flag — not proof it learned anything.
It feels intuitive to judge a model by how well it fits the data you trained it on. It's also one of the most reliable ways to fool yourself. A model can score almost perfectly on its training set two very different ways: by genuinely learning the real underlying pattern, or by simply memorizing the specific rows, noise, and quirks of that exact dataset. Only one of those generalizes to new data. Telling them apart is the entire point of the underfitting/overfitting framework — and it's why no serious model is ever judged on training performance alone.