OverSampling is a technique used in data analysis to address the problem of imbalanced datasets, where one class has significantly more samples than another. By increasing the number of instances in the minority class, OverSampling helps improve the performance of machine learning models, making them more accurate in predicting outcomes for underrepresented groups. There are various methods for OverSampling, such as duplicating existing minority class samples or generating new synthetic samples using algorithms like SMOTE (Synthetic Minority Over-sampling Technique). This process ensures that the model learns effectively from all classes, leading to better generalization and reduced bias in predictions.