As @danjodc already pointed out, SCF=QC is not an approximation but rather a way of solving the SCF equations. The SCF equations arise from the extremality condition for the orbitals that minimize energy (see e.g. our open access review https://www.mdpi.com/1420-3049/25/5/1218). Instead of solving the SCF equations by iterative re-diagonalization, you can just optimize the orbitals directly. This is essentially what SCF=QC does in Gaussian.
The diagonalization based approach tends to be very fast for "easy" systems, because of Pulay's DIIS procedure that is often able to find the ground state solution quickly. However, in cases where DIIS fails to lead to reliable convergence, energy minimization may be a better alternative.
I don't remember by heart what kind of algorithm Gaussian uses in SCF=QC, but the most famous quadratic convergence algorithm i.e. Newton-Raphson algorithm is also infamous for converging to saddle-point solutions. Moreover, direct energy minimization may only lead you to the closest minimum, not the global minimum.
If you are experiencing convergence problems, I recommend the XQC algorithm which is sort of the best of both worlds: it uses DIIS for starters, and only when DIIS does not converge it switches over to quadratic convergence. If you're dealing with metals, you might also try toying around with
SCF=Fermi which might help in locating the best orbitals. Moreover, running the calculation in a smaller basis first and reading it in as guess for the "production calculation" may also help in finding the converged solution.
On a final note, metal complexes may prefer higher spin states. It might be good to check how the energy behaves with respect to the spin state: does it go down when you do e.g. singlet -> triplet -> quintet -> septet -> nonet -> etc?