Adaptive step size is a technique used in numerical methods to adjust the size of the steps taken during calculations. This approach helps improve accuracy and efficiency by increasing the step size when the solution is changing slowly and decreasing it when the solution is changing rapidly. This flexibility allows for better handling of complex problems. In the context of algorithms, such as those used in machine learning or differential equations, adaptive step size can lead to faster convergence and reduced computational costs. By dynamically adjusting the step size, these algorithms can achieve more precise results without unnecessary calculations.