What is overfitting in the context of machine learning?

A$)$ When a model performs well on training data but poorly on unseen data.

B$)$ When a model performs well on both training and test data.

C$)$ When a model underestimates the complexity of the data.

D$)$ When a model fails to capture patterns in the training data.