In recent years, identification of nonlinear dynamical systems from data has become increasingly popular. Sparse regression approaches, such as sparse identification of nonlinear dynamics (SINDy), fostered the development of novel governing equation identification algorithms assuming the state variables are known a priori and the governing equations lend themselves to sparse, linear expansions in a (nonlinear) basis of the state variables. In the context of the identification of governing equations of nonlinear dynamical systems, one faces the problem of identifiability of model parameters when state measurements are corrupted by noise. Measurement noise affects the stability of the recovery process yielding incorrect sparsity patterns and inaccurate estimation of coefficients of the governing equations. In this work, we investigate and compare the performance of several local and global smoothing techniques to a priori denoise the state measurements and numerically estimate the state-time derivatives to improve the accuracy and robustness of two sparse regression methods to recover governing equations: sequentially thresholded least squares (STLS) and weighted basis pursuit denoising (WBPDN) algorithms. We empirically show that, in general, global methods, which use the entire measurement data set, outperform local methods, which employ a neighboring data subset around a local point. We additionally compare generalized cross-validation (GCV) and Pareto curve criteria as model selection techniques to automatically estimate near optimal tuning parameters and conclude that Pareto curves yield better results. The performance of the denoising strategies and sparse regression methods is empirically evaluated through well-known benchmark problems of nonlinear dynamical systems.