The model does not correctly recognize patterns but “learns the training data by heart”.
We can observe it if the validation loss is higher than the training loss e.g.
Overfitting == memorization.
Overfitting means high variance: It tells us the amount the prediction will change if you change the training data (sensitivity).
High variance can come from lots of parameters / flexibility (without regularization).