Function approximation is a mathematical technique used to estimate complex functions using simpler ones. This is often necessary when the exact function is difficult to work with or unknown. By using methods like polynomial fitting or neural networks, we can create a model that closely resembles the behavior of the original function over a specific range of inputs. In many fields, such as machine learning and statistics, function approximation helps in making predictions or decisions based on data. For example, a regression model can approximate the relationship between variables, allowing us to understand trends and make informed choices based on the estimated function.