What describes a situation where a machine learning model performs poorly on training data due to being too basic?

The situation where a machine learning model performs poorly on the training data due to being too basic is known as underfitting. Underfitting occurs when a model is too simple to capture the underlying trends in the data. This can happen if the model has too few parameters, is not complex enough, or fails to account for the patterns present in the training dataset. As a result, the model cannot learn effectively from the training data, leading to high error rates on both the training set and the validation/test sets.

In contrast, overfitting refers to a model that learns the details and noise in the training data to the point where it negatively impacts its performance on new data. Generalization is the model's ability to perform well on unseen data, which is not the case when a model underfits. Regularization is a technique used to prevent overfitting by adding a penalty for complexity to the model, which is not directly related to the concept of underfitting. Thus, underfitting accurately describes the scenario presented in the question.