Gradient-based One-Side Sampling (GOSS) is a key innovation in LightGBM that accelerates model training without losing accuracy. By focusing on high-gradient (hard-to-learn) data points and selectively sampling low-gradient ones, GOSS strikes the perfect balance between speed and performance, making LightGBM faster and more efficient than traditional boosting methods.