Underfitting occurs when a model is too simple to capture the underlying patterns in the data, performing poorly on both training and test data. Unlike overfitting (which memorizes), underfitting fails to learn at all — like a student who doesn't study enough and scores poorly on every exam. Causes include an overly simple model, insufficient training time, or inadequate features. Remedies include using a more complex model, training longer, adding features, or reducing regularization.