Overfitting is when a model memorizes the training questions instead of actually learning — it then fails on anything new.
Imagine it like this: A student who memorizes the exact answers of last year's mock exam will pass that same exam again — but will fail the real exam because the questions are worded differently. They didn't learn, they just memorized.
Examples:
So? A model with a perfect score on training data but a bad score on new data hasn't really learned — it just memorized. That's a big red flag!