What term describes a model that performs well on any new datasets it might encounter?

Generalization is the term used to describe a model's ability to perform well on new, unseen datasets that it has not encountered during training. A model that generalizes effectively captures the underlying patterns in the training data without being overly influenced by noise or specific examples. When a model generalizes well, it maintains high accuracy and performs reliably on various datasets, demonstrating its robustness and utility in real-world applications.

This ability is crucial in data science, as the ultimate goal is not just to achieve high accuracy on the training data but to ensure that the model can make accurate predictions on new data. A well-generalized model avoids being too tailored to the training dataset, which can occur with practices like overfitting, where the model learns the training data too well, including its noise and outliers, leading to poor performance on new data. Generalization, therefore, represents the ideal scenario where a model balances complexity and simplicity, accurately reflecting the broader trends in the data.