Synthetic data in machine learning refers to artificially generated data that mimics real-world data characteristics while preserving privacy and security. This technique is often used when access to legitimate data is limited due to privacy concerns or data availability issues. Synthetic data can supplement or replace real data in training machine learning models, enabling developers to create robust algorithms that perform well in various scenarios without exposing sensitive information.