  Accurate detection of the cosmological 21-cm global signal requires galactic
foreground models which can remove power over ~$10^6$. Although foreground and
global signal models unavoidably exhibit overlap in their vector-spaces
inducing bias error in the extracted signal, a second source of bias and error
arises from inadequate foreground models, i.e. models which cannot fit spectra
down to the noise level of the signal. We therefore test the level to which
seven commonly employed foreground models -- including nonlinear and linear
forward-models, polynomials, and maximally-smooth polynomials -- fit realistic
simulated mock foreground spectra, as well as their dependence upon model
inputs. The mock spectra are synthesized for an EDGES-like experiment and we
compare all models' goodness-of-fit and preference using a Kolomogorov-Smirnov
test of the noise-normalized residuals in order to compare models with
differing, and sometimes indeterminable, degrees of freedom. For a single LST
bin spectrum and p-value threshold of $p=0.05$, the nonlinear-forward model
with 4 parameters is preferred ($p=0.99$), while the linear forward-model fits
well with 6-7 parameters ($p=0.94,0.97$ respectively). The polynomials and
maximally-smooth polynomials, like those employed by the EDGES and SARAS3
experiments, cannot produce good fits with 5 parameters for the experimental
simulations in this work ($p<10^{-6}$). However, we find that polynomials with
6 parameters pass the KS-test ($p=0.4$), although a 9 parameter fit produces
the highest p-value ($p\sim0.67$). When fitting multiple LST bins
simultaneously, we find that the linear forward-model outperforms (a higher
p-value) the nonlinear for 2, 5 and 10 LST bins. Importantly, the KS-test
consistently identifies best-fit \textit{and} preferred models.
