The shrinkage is generally useful, which is why it is enabled by default. Full methods are described in the DESeq2 paper (see DESeq2 citation), but in short, it looks at the largest fold changes that are not due to low counts and uses these to inform a prior distribution. So the large fold changes from genes with lots of statistical information are not shrunk, while the imprecise fold changes are shrunk. This allows you to compare all estimated LFC across experiments, for example, which is not really feasible without the use of a prior.
One case where I would not use it, is if it is expected that nearly all genes will have no change and there is little to no variation across replicates (so near technical replication), and then say < 10 genes with very large fold changes. This scenario could occur in non-biological samples, for example technical replicates plus DE spike ins. The reason this would cause a problem is that the prior is formed according to a high percentile of the large fold changes, but it could miss if there were singular DE genes, and form a prior which is not wide enough to accommodate very large fold changes. It is trivial to turn off the prior in this case (betaPrior=FALSE).
I don't have a comment on small RNA-seq, as I haven't personally analyzed this, but I know the moderated LFC have been used in some small RNA-seq analyses.
You can plot fold changes with and without shrinkage like so:
res <- results(dds, addMLE=TRUE)