Final answer:
A good model must accurately represent the real world, make predictions that match observations, and fulfill its purpose. Evaluation involves assessing limitations, testing hypotheses, and comparing to other models and data.
Step-by-step explanation:
A good model must not only fit the training data, but also accurately represent the real world and make predictions that match observations. The usefulness of a model is determined by how well it can predict future changes and fulfill the intended purpose. For example, in the field of ecology, an analytical model is considered ecologically more realistic and can be used to describe population changes and predict future outcomes.
The evaluation of a model involves assessing its limitations, testing hypotheses, and comparing it to other models and available data. It is important to note that even when a model's predictions match real-world observations, it doesn't guarantee its correctness or exclusivity.
Overall, models should be constructed properly, well-documented, and consistent with available data and existing theories to be considered good models.