diff --git a/src/GLMNet.jl b/src/GLMNet.jl
index cd21242..cc94f43 100644
--- a/src/GLMNet.jl
+++ b/src/GLMNet.jl
@@ -204,9 +204,9 @@ modeltype(::Normal) = "Least Squares"
 modeltype(::Binomial) = "Logistic"
 modeltype(::Poisson) = "Poisson"
 
-function show(io::IO, g::GLMNetPath)
+function show(io::IO, ::MIME"text/plain", g::GLMNetPath)
     println(io, "$(modeltype(g.family)) GLMNet Solution Path ($(size(g.betas, 2)) solutions for $(size(g.betas, 1)) predictors in $(g.npasses) passes):")
-    print(io, CoefTable(Union{Vector{Int},Vector{Float64}}[nactive(g.betas), g.dev_ratio, g.lambda], ["df", "pct_dev", "λ"], []))
+    show(io, MIME"text/plain"(), CoefTable(Union{Vector{Int},Vector{Float64}}[nactive(g.betas), g.dev_ratio, g.lambda], ["df", "pct_dev", "λ"], []))
 end
 
 function check_jerr(jerr, maxit, pmax)
diff --git a/test/runtests.jl b/test/runtests.jl
index 25b34f2..59d662f 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -172,8 +172,85 @@ cv = glmnetcv(X, y; folds=[1,1,1,1,2,2,2,3,3,3])
 glmnetcv(X, y, nfolds = 3)
 
 # Make sure show works
-show(IOBuffer(), cv)
-show(IOBuffer(), cv.path)
+@test repr("text/plain", cv) == """
+Least Squares GLMNet Cross Validation
+69 models for 6 predictors in 3 folds
+Best λ 4.111 (mean loss 121.760, std 16.875)"""
+@test repr("text/plain", cv.path) == """
+Least Squares GLMNet Solution Path (69 solutions for 6 predictors in 330 passes):
+──────────────────────────────
+      df   pct_dev           λ
+──────────────────────────────
+ [1]   0  0.0       31.8289
+ [2]   1  0.161141  29.0013
+ [3]   1  0.294923  26.4249
+ [4]   1  0.405991  24.0774
+ [5]   1  0.498201  21.9384
+ [6]   1  0.574756  19.9895
+ [7]   1  0.638313  18.2137
+ [8]   1  0.69108   16.5956
+ [9]   1  0.734887  15.1213
+[10]   1  0.771257  13.778
+[11]   1  0.801452  12.554
+[12]   1  0.82652   11.4387
+[13]   1  0.847332  10.4225
+[14]   1  0.864611   9.49663
+[15]   1  0.878956   8.65297
+[16]   1  0.890865   7.88427
+[17]   1  0.900753   7.18385
+[18]   1  0.908961   6.54566
+[19]   1  0.915776   5.96416
+[20]   1  0.921434   5.43432
+[21]   1  0.926132   4.95155
+[22]   1  0.930031   4.51167
+[23]   1  0.933269   4.11086
+[24]   1  0.935957   3.74566
+[25]   2  0.939866   3.41291
+[26]   2  0.945705   3.10972
+[27]   2  0.950551   2.83346
+[28]   2  0.954574   2.58174
+[29]   2  0.957915   2.35239
+[30]   2  0.960688   2.14341
+[31]   2  0.96299    1.95299
+[32]   4  0.966326   1.77949
+[33]   4  0.970479   1.62141
+[34]   5  0.974063   1.47737
+[35]   5  0.977328   1.34612
+[36]   5  0.980032   1.22654
+[37]   5  0.982277   1.11757
+[38]   5  0.984142   1.01829
+[39]   5  0.985689   0.92783
+[40]   5  0.986968   0.845404
+[41]   5  0.988036   0.770301
+[42]   5  0.988922   0.701869
+[43]   6  0.989839   0.639517
+[44]   6  0.990751   0.582704
+[45]   6  0.991518   0.530938
+[46]   6  0.992154   0.483771
+[47]   6  0.992683   0.440794
+[48]   6  0.993116   0.401635
+[49]   6  0.993481   0.365955
+[50]   6  0.993785   0.333445
+[51]   6  0.994037   0.303822
+[52]   6  0.994242   0.276832
+[53]   6  0.994415   0.252239
+[54]   6  0.99456    0.229831
+[55]   6  0.99468    0.209413
+[56]   6  0.994777   0.190809
+[57]   6  0.99486    0.173858
+[58]   6  0.994929   0.158413
+[59]   6  0.994987   0.14434
+[60]   6  0.995035   0.131517
+[61]   6  0.995072   0.119834
+[62]   6  0.995105   0.109188
+[63]   6  0.995133   0.0994882
+[64]   6  0.995156   0.0906499
+[65]   6  0.995175   0.0825968
+[66]   6  0.995191   0.0752591
+[67]   6  0.995204   0.0685733
+[68]   6  0.995215   0.0624815
+[69]   6  0.995224   0.0569308
+──────────────────────────────"""
 show(IOBuffer(), cv.path.betas) # this catches the showarray issue
 
 # Passing RNG makes cv deterministic