Otsu's Method is a widely used technique in image processing for automatic threshold selection, aimed at separating foreground and background in grayscale images by minimizing intra-class variance. It is particularly effective for bimodal histograms, where it efficiently determines the optimal threshold that maximizes inter-class variance, making it a fundamental tool in segmentation tasks.