Synthetic data generation involves creating artificial datasets that mimic real-world data properties. This is beneficial in scenarios where privacy is a concern, or a specific data sample is rare or expensive to obtain. The synthetic data can be generated using simulations, mathematical models, or machine learning algorithms. It is widely used in testing data-hungry algorithms, helping to avoid biases that might come with overfitting to limited real data.