Oversampling is a technique used in data analysis and machine learning to address class imbalance in datasets. When one class has significantly fewer examples than another, it can lead to biased models. Oversampling involves increasing the number of instances in the minority class, often by duplicating existing data points or generating synthetic examples using methods like SMOTE (Synthetic Minority Over-sampling Technique). This approach helps improve the performance of models by providing them with more balanced training data. By ensuring that the minority class is adequately represented, oversampling can lead to better predictions and a more reliable understanding of the underlying patterns in the data.