Clustering with scikit-learn involves selecting suitable algorithms like K-Means, DBSCAN, and Agglomerative Clustering. Key steps include determining parameters such as number of clusters, eps, and min_samples, fitting models, assigning cluster labels, and visualizing results using PCA components for data insight.