Oversampling techniques are methods used in data analysis to address class imbalance in datasets, where one class has significantly fewer instances than another. This imbalance can lead to biased models that perform poorly on the minority class. By artificially increasing the number of instances in the minority class, these techniques help create a more balanced dataset, improving the model's ability to learn from all classes. Common oversampling methods include Random Oversampling, where duplicates of minority class instances are added, and SMOTE (Synthetic Minority Over-sampling Technique), which generates new synthetic instances based on existing minority class data. These techniques aim to enhance model performance and ensure fair representation of all classes in predictive tasks.