Data-driven reconstruction of biological networks is a crucial step towards making sense of large volumes of biological data. While several methods have been developed recently to reconstruct biological networks, there are few systematic and comprehensive studies that compare different methods in terms of their ability to handle incomplete data sets, high data dimensions, and noisy data. We use experimentally measured and synthetic data sets to compare three popular methods--principal component regression (PCR), linear matrix inequalities (LMI), and Least Absolute Shrinkage and Selection Operator (LASSO)--in terms of root-mean-squared-error (RMSE), average fractional error in the value of the coefficients, accuracy, sensitivity, specificity and the geometric mean of sensitivity and specificity. This comparison enables us to establish criteria for selection of an appropriate approach for network reconstruction based on a priori properties of experimental data. For instance, while PCR is the fastest method, LASSO and LMI perform better in terms of accuracy, sensitivity and specificity. Both PCR and LASSO are better than LMI in terms of fractional error in the values of the computed parameters. Trade-offs such as these suggests that more than one aspect of each method needs to be taken into account when designing strategies for network reconstruction.