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 to balance the dataset by reducing the number of samples from the majority class, making it more equal in size to the minority class. This method can improve the performance of algorithms by ensuring they learn from a more balanced representation of the data. 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 determine the right amount of undersampling to apply.