Synthetic data generation involves creating artificial data that mimics real-world data, allowing researchers and developers to train and test machine learning models without compromising privacy or needing large amounts of real data. This technique is crucial for overcoming data scarcity, enhancing model robustness, and ensuring compliance with data protection regulations.