Interatomic potentials have been widely used to accelerate atomistic simulations such as molecular dynamics for a long time. Recently, frameworks to build an accurate interatomic potential were proposed, combining a systematic set of density functional theory (DFT) calculations with machine learning techniques. One of these frameworks is to use the compressed sensing deriving a sparse representation for the interatomic potential. This facilitates the control of the accuracy of interatomic potentials. In this study, we demonstrate the applicability of the compressed sensing interatomic potential to ten elemental metals of Ag, Al, Au, Ca, Cu, Ga, In, K, Li and Zn. For each elemental metal, an interatomic potential is made from thousands of DFT calculations using the elastic net regression. Not only they have prediction errors with an order of magnitude smaller than 4 meV/atom, but also they can well predict physical properties such as lattice constants and phonon dispersion relationship.