Kernel smoothing is a statistical technique used to estimate the underlying structure of data by creating a smooth curve through a set of points. It works by placing a "kernel" function, which is a weighted average, over each data point. The influence of each point decreases with distance, allowing for a smooth representation of the data without assuming a specific model. This method is particularly useful in situations where data is noisy or irregularly spaced. By adjusting the bandwidth of the kernel, users can control the level of smoothness, balancing between capturing important trends and avoiding overfitting to random fluctuations in the data.