LOWESS, or Locally Weighted Scatterplot Smoothing, is a non-parametric regression method used to create a smooth line through a scatterplot of data points. It works by fitting multiple regressions in localized subsets of the data, allowing for flexibility in capturing trends without assuming a specific global model. This technique is particularly useful for visualizing relationships in data that may not follow a linear pattern. By adjusting the weight of nearby points, LOWESS can provide a clearer representation of the underlying trend, making it easier to identify patterns and anomalies in complex datasets.