UnderSampling is a technique used in data processing, 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. UnderSampling reduces the number of samples from the majority class to create a more balanced dataset. This method helps improve the performance of algorithms by ensuring that they learn equally from both classes. However, it can 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.