  The Universal Rotation Curve (URC) of disk galaxies was originally proposed
to predict the shape and amplitude of any rotation curve (RC) based solely on
photometric data. Here, the URC is investigated with an extensive set of
spatially-resolved rotation curves drawn from the PROBES-I, PROBES-II, and
MaNGA data bases with matching multi-band surface brightness profiles from the
DESI-LIS and WISE surveys for 3,846 disk galaxies. Common URC formulations fail
to achieve an adequate level of accuracy to qualify as truly universal over
fully sampled RCs. We develop neural network (NN) equivalents for the proposed
URCs which predict RCs with higher accuracy, showing that URC inaccuracies are
not due to insufficient data but rather non-optimal formulations or sampling
effects. This conclusion remains even if the total RC sample is pruned for
symmetry. The latest URC prescriptions and their NN equivalents trained on our
sub-sample of 579 disk galaxies with symmetric RCs perform similarly to the
URC/NN trained on the complete data sample. We conclude that a URC with an
acceptable level of accuracy ($\Delta V_{\rm circ} \lesssim15$ per cent) at all
radii would require a detailed modelling of a galaxy's central regions and
outskirts (e.g., for baryonic effects leading to contraction or expansion of
any dark-matter-only halo).
