diff --git a/python/lsst/pipe/tasks/maskStreaks.py b/python/lsst/pipe/tasks/maskStreaks.py
index 9cf75fee0f..7a0f7ce9b8 100644
--- a/python/lsst/pipe/tasks/maskStreaks.py
+++ b/python/lsst/pipe/tasks/maskStreaks.py
@@ -359,7 +359,7 @@ def _lineChi2(self, line, grad=True):
         reducedHessianChi = hessianChi2 / self.lineMaskSize
         return reducedChi, reducedDChi, reducedHessianChi
 
-    def fit(self, dChi2Tol=0.1, maxIter=100):
+    def fit(self, dChi2Tol=0.1, maxIter=100, log=None):
         """Perform Newton-Raphson minimization to find line parameters.
 
         This method takes advantage of having known derivative and Hessian of
@@ -378,6 +378,7 @@ def fit(self, dChi2Tol=0.1, maxIter=100):
             Maximum number of fit iterations allowed. The fit should converge in
             ~10 iterations, depending on the value of dChi2Tol, but this
             maximum provides a backup.
+        log : TODO
 
         Returns
         -------
@@ -407,14 +408,17 @@ def line_search(c, dx):
             if chi2 == 0:
                 break
             if not np.isfinite(A).all():
-                # TODO: DM-30797 Add warning here.
                 fitFailure = True
+                if log is not None:
+                    log.warning("Hessian matrix has non-finite elements.")
                 break
             dChi2 = oldChi2 - chi2
             try:
                 cholesky = scipy.linalg.cho_factor(A)
             except np.linalg.LinAlgError:
                 fitFailure = True
+                if log is not None:
+                    log.warning("Hessian matrix is not invertible.")
                 break
             dx = scipy.linalg.cho_solve(cholesky, b)
 
@@ -651,6 +655,7 @@ def _runKHT(self, image):
                                     self.config.clusterMinimumDeviation, self.config.delta,
                                     self.config.minimumKernelHeight, self.config.nSigma,
                                     self.config.absMinimumKernelHeight)
+        self.log.info("The Kernel Hough Transform detected %s line(s)", len(lines))
 
         return LineCollection(lines.rho, lines.theta)
 
@@ -701,6 +706,7 @@ def _findClusters(self, lines):
         finalRhos = finalClusters[0] * self.config.rhoBinSize
         finalThetas = finalClusters[1] * self.config.thetaBinSize
         result = LineCollection(finalRhos, finalThetas)
+        self.log.info("Lines were grouped into %s potential streak(s)", len(finalRhos))
 
         return result
 
@@ -732,6 +738,7 @@ def _fitProfile(self, lines, maskedImage):
 
         lineFits = LineCollection([], [])
         finalLineMasks = [np.zeros(data.shape, dtype=bool)]
+        nFinalLines = 0
         for line in lines:
             line.sigma = self.config.invSigma**-1
             lineModel = LineProfile(data, weights, line=line)
@@ -739,17 +746,23 @@ def _fitProfile(self, lines, maskedImage):
             if lineModel.lineMaskSize == 0:
                 continue
 
-            fit, chi2, fitFailure = lineModel.fit(dChi2Tol=self.config.dChi2Tolerance)
+            fit, chi2, fitFailure = lineModel.fit(dChi2Tol=self.config.dChi2Tolerance, log=self.log)
+            if fitFailure:
+                self.log.warning("Streak fit failed.")
 
             # Initial estimate should be quite close: fit is deemed unsuccessful if rho or theta
             # change more than the allowed bin in rho or theta:
             if ((abs(fit.rho - line.rho) > 2 * self.config.rhoBinSize)
                     or (abs(fit.theta - line.theta) > 2 * self.config.thetaBinSize)):
                 fitFailure = True
+                self.log.warning("Streak fit moved too far from initial estimate. Line will be dropped.")
 
             if fitFailure:
                 continue
 
+            self.log.debug("Best fit streak parameters are rho=%.2f, theta=%.2f, and sigma=%.2f", fit.rho,
+                           fit.theta, fit.sigma)
+
             # Make mask
             lineModel.setLineMask(fit)
             finalModel = lineModel.makeProfile(fit)
@@ -763,7 +776,12 @@ def _fitProfile(self, lines, maskedImage):
             fit.finalModelMax = finalModelMax
             lineFits.append(fit)
             finalLineMasks.append(finalLineMask)
+            nFinalLines += 1
 
         finalMask = np.array(finalLineMasks).any(axis=0)
+        nMaskedPixels = finalMask.sum()
+        percentMasked = (nMaskedPixels / finalMask.size) * 100
+        self.log.info("%d streak(s) fit, with %d pixels masked (%0.2f%% of image)", nFinalLines, nMaskedPixels,
+                      percentMasked)
 
         return lineFits, finalMask