Undersampling is a technique used in data analysis, particularly in the context of machine learning, to address class imbalance in datasets. When one class has significantly more samples than another, it can lead to biased models that perform poorly on the minority class. Undersampling helps by reducing the number of samples from the majority class, making the dataset more balanced. This method can improve the performance of algorithms by ensuring that they learn equally from both classes. However, it may also result in the loss of valuable information, as some data points from the majority class are discarded. Careful consideration is needed to balance the benefits and drawbacks of undersampling.