  Most dark energy models have the $\Lambda$CDM as their limit, and if future
observations constrain our universe to be close to $\Lambda$CDM Bayesian
arguments about the evidence and the fine-tuning will have to be employed to
discriminate between the models. Assuming a baseline $\Lambda$CDM model we
investigate a number of quintessence and phantom dark energy models, and we
study how they would perform when compared to observational data, such as the
expansion rate, the angular distance, and the growth rate measurements, from
the upcoming Dark Energy Spectroscopic Instrument (DESI) survey. We sample
posterior likelihood surfaces of these dark energy models with Monte Carlo
Markov Chains while using central values consistent with the Planck
$\Lambda$CDM universe and covariance matrices estimated with Fisher information
matrix techniques. We find that for this setup the Bayes factor provides a
substantial evidence in favor of the $\Lambda$CDM model over most of the
alternatives. We also investigated how well the CPL parametrization
approximates various scalar field dark energy models, and identified the
location for each dark energy model in the CPL parameter space.
