Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the data. This means that the model fails to learn enough from the training data, resulting in poor performance on both the training and test datasets. For example, if a model is trying to predict house prices but only considers the number of bedrooms, it may miss other important factors like location or size. When a model is underfitting, it often produces high errors and lacks accuracy. To improve the situation, one can use more complex algorithms or include additional features, such as location and square footage, to help the model better understand the data and make more accurate predictions.