It was recently shown that networked MIMO radars with sparse sensing and matrix completion (MC) can significantly reduce the volume of data required for accurate target detection and estimation. Based on the target returns, forwarded by the receive antennas to a fusion center, a matrix can be formulated and used in standard array processing methods to estimate the target parameters. For a small number of targets, the aforementioned matrix is low-rank and thus can be recovered from a small subset of its elements using MC. This allows for sparse sensing at the receive antennas, and subsequently populating the data matrix in a uniformly sparse fashion. This paper studies the applicability of MC theory on the data matrices that arise in colocated MIMO radars using uniform linear arrays. It is shown that the coherence is directly related to transmit waveforms, and that when the waveforms are orthogonal the optimum choice is for them to be spatial white noise-type functions in all snapshots.