Underfitting is a modeling error that occurs when a model is too simple to capture the underlying pattern of the data. This happens when the model has too few parameters or is overly constrained, leading to high bias and poor performance on both the training and test datasets. Underfitting results in a model that fails to learn the complexities of the data, producing inaccurate and unreliable predictions. Techniques to address underfitting include increasing the model complexity, adding more features, and reducing regularization. Balancing model complexity to avoid underfitting while preventing overfitting is crucial for developing robust predictive models.