Splines are an attractive way of flexibly modeling a regression curve since their basis functions can be included like ordinary covariates in regression settings. An overview of least squares regression using splines is presented including many graphical illustrations and comprehensive examples. Starting from two bases that are widely used for constructing splines, three different variants of splines are discussed: simple regression splines, penalized splines and smoothing splines. Further, restrictions such as monotonicity constraints are considered. The presented spline variants are illustrated and compared in a bivariate and a multivariate example with well-known data sets. A brief computational guide for practitioners using the open-source software R is given.