The Parzen-window method works by placing a kernel function, often a Gaussian kernel, at each data point in the dataset. The contributions from all kernels are then summed to estimate the density at a given point. This non-parametric approach allows for flexible and smooth density estimation, making it particularly useful for detecting patterns and structures in data, as well as for clustering and classification tasks.