Random Oversampling is a technique used in machine learning to address the problem of imbalanced datasets, where one class has significantly more samples than another. This method involves duplicating instances from the minority class to create a more balanced dataset, allowing algorithms to learn better from both classes. By increasing the representation of the minority class, Random Oversampling helps improve the performance of models, particularly in classification tasks. However, it can also lead to overfitting, as the same samples are repeated, which may not provide new information for the model to learn.