Why is feature scaling important in machine learning?

Feature scaling is crucial in machine learning because it standardizes the range of independent variables or features of data. This helps in optimizing the performance of algorithms that depend on the distance between data points, such as k-nearest neighbors and gradient-based algorithms like gradient descent. If features have different units or scales, those with larger scales can disproportionately influence the model's prediction, leading to suboptimal performance. Common methods of feature scaling include normalization (scaling features to a range of [0,1]) and standardization (scaling features to have a mean of 0 and a standard deviation of 1).