We compared the ML predictions to T1/2 predictions of DFAs for three previously identified 40 best-performing DFAs, i.e., modB3LYP (aHF = 0.10), TPSSh, and M06-L. These functionals predicted SCO behavior of most complexes by correctly predicting their free energies of spin transition but with poor predictions of T1/2 values. We observed that the DFA T1/2 predictions had much higher MAEs than the ML models albeit with relatively comparable Pearson's r correlation between DFT and experimental T1/2 values as compared to that between ML T1/2 predictions and experimental T1/2 values. To further validate the ML model approach, we studied ML model predictions on the set of 18 SCO complexes for which experimental T1/2 values are not reported by estimating their T1/2 values from the average of low-T and high-T of crystallization. Upon excluding the SCO complexes that showed two-step SCO behavior and one containing boron, the RF-RFA RF model showed stronger correlation to estimated T1/2 values over the resulting set of 9 complexes with a Pearson's r of 0.82. However, DFA-predicted T1/2 values had large errors and showed no correlation to estimated T1/2 values over the set of 18 SCO complexes nor over the smaller set of 9 SCO complexes which solely exhibit single-step SCO behavior. Overall, our study of ML and DFA T1/2 predictions of Fe(II) SCO complexes in the SCO-95 data set highlights that random forest models show reasonable performance comparable to some of the best performing density functionals at much lower computational cost. We expect this performance to improve as larger datasets of experimental SCOs are curated. Alternatively, inclusion of DFT-predicted quantities from best-performing functionals could also be used to increase model performance, albeit at higher computational cost.